Enhanced descriptions from Syndetics:

This volume contains precise descriptions of the many related Six Sigma methods along with details of case studies showing how these methods have been applied in engineering and business to achieve substantial savings.

Table of contents provided by Syndetics

• List of Acronyms (p. xxi)
• 1 Introduction (p. 1)
• 1.1 Purpose of this Book (p. 1)
• 1.2 Systems and Key Input Variables (p. 2)
• 1.3 Problem-solving Methods (p. 6)
• 1.3.1 What Is "Six Sigma"? (p. 7)
• 1.4 History of "Quality" and Six Sigma (p. 10)
• 1.4.1 History of Management and Quality (p. 10)
• 1.4.2 History of Documentation and Quality (p. 14)
• 1.4.3 History of Statistics and Quality (p. 14)
• 1.4.4 The Six Sigma Movement (p. 17)
• 1.5 The Culture of Discipline (p. 18)
• 1.6 Real Success Stories (p. 20)
• 1.7 Overview of this Book (p. 21)
• 1.8 References (p. 22)
• 1.9 Problems (p. 22)
• Part I Statistical Quality Control
• 2 Statistical Quality Control and Six Sigma (p. 29)
• 2.1 Introduction (p. 29)
• 2.2 Method Names as Buzzwords (p. 30)
• 2.3 Where Methods Fit into Projects (p. 31)
• 2.4 Organizational Roles and Methods (p. 33)
• 2.5 Specifications: Nonconforming vs Defective (p. 34)
• 2.6 Standard Operating Procedures (SOPs) (p. 36)
• 2.6.1 Proposed SOP Process (p. 37)
• 2.6.2 Measurement SOPs (p. 40)
• 2.7 References (p. 40)
• 2.8 Problems (p. 41)
• 3 Define Phase and Strategy (p. 45)
• 3.1 Introduction (p. 45)
• 3.2 Systems and Subsystems (p. 46)
• 3.3 Project Charters (p. 47)
• 3.3.1 Predicting Expected Profits (p. 50)
• 3.4 Strategies for Project Definition (p. 51)
• 3.4.1 Bottleneck Subsystems (p. 51)
• 3.4.2 Go-no-go Decisions (p. 52)
• 3.5 Methods for Define Phases (p. 53)
• 3.5.1 Pareto Charting (p. 53)
• 3.5.2 Benchmarking (p. 56)
• 3.6 Formal Meetings (p. 58)
• 3.7 Significant Figures (p. 60)
• 3.8 Chapter Summary (p. 63)
• 3.9 References (p. 65)
• 3.10 Problems (p. 65)
• 4 Measure Phase and Statistical Charting (p. 75)
• 4.1 Introduction (p. 75)
• 4.2 Evaluating Measurement Systems (p. 76)
• 4.2.1 Types of Gauge R&R Methods (p. 77)
• 4.2.2 Gauge R&R: Comparison with Standards (p. 78)
• 4.2.3 Gauge R&R (Crossed) with Xbar & R Analysis (p. 81)
• 4.3 Measuring Quality Using SPC Charting (p. 85)
• 4.3.1 Concepts: Common Causes and Assignable Causes (p. 86)
• 4.4 Commonality: Rational Subgroups, Control Limits, and Startup (p. 87)
• 4.5 Attribute Data: p-Charting (p. 89)
• 4.6 Attribute Data: Demerit Charting and u-Charting (p. 94)
• 4.7 Continuous Data: Xbar & R Charting (p. 98)
• 4.7.1 Alternative Continuous Data Charting Methods (p. 104)
• 4.8 Chapter Summary and Conclusions (p. 105)
• 4.9 References (p. 107)
• 4.10 Problems (p. 107)
• 5 Analyze Phase (p. 117)
• 5.1 Introduction (p. 117)
• 5.2 Process Mapping and Value Stream Mapping (p. 117)
• 5.2.1 The Toyota Production System (p. 120)
• 5.3 Cause and Effect Matrices (p. 121)
• 5.4 Design of Experiments and Regression (Preview) (p. 123)
• 5.5 Failure Mode and Effects Analysis (p. 125)
• 5.6 Chapter Summary (p. 128)
• 5.7 References (p. 129)
• 5.8 Problems (p. 129)
• 6 Improve or Design Phase (p. 135)
• 6.1 Introduction (p. 135)
• 6.2 Informal Optimization (p. 136)
• 6.3 Quality Function Deployment (QFD) (p. 137)
• 6.4 Formal Optimization (p. 140)
• 6.5 Chapter Summary (p. 143)
• 6.6 References (p. 143)
• 6.7 Problems (p. 143)
• 7 Control or Verify Phase (p. 147)
• 7.1 Introduction (p. 147)
• 7.2 Control Planning (p. 148)
• 7.3 Acceptance Sampling (p. 151)
• 7.3.1 Single Sampling (p. 152)
• 7.3.2 Double Sampling (p. 153)
• 7.4 Documenting Results (p. 155)
• 7.5 Chapter Summary (p. 156)
• 7.6 References (p. 157)
• 7.7 Problems (p. 157)
• 8 Advanced SQC Methods (p. 161)
• 8.1 Introduction (p. 161)
• 8.2 EWMA Charting for Continuous Data (p. 162)
• 8.3 Multivariate Charting Concepts (p. 165)
• 8.4 Multivariate Charting (Hotelling's T[superscript 2] Charts) (p. 168)
• 8.5 Summary (p. 172)
• 8.6 References (p. 172)
• 8.7 Problems (p. 172)
• 9 SQC Case Studies (p. 175)
• 9.1 Introduction (p. 175)
• 9.2 Case Study: Printed Circuit Boards (p. 175)
• 9.2.1 Experience of the First Team (p. 177)
• 9.2.2 Second Team Actions and Results (p. 179)
• 9.3 Printed Circuitboard: Analyze, Improve, and Control Phases (p. 181)
• 9.4 Wire Harness Voids Study (p. 184)
• 9.4.1 Define Phase (p. 185)
• 9.4.2 Measure Phase (p. 185)
• 9.4.3 Analyze Phase (p. 187)
• 9.4.4 Improve Phase (p. 188)
• 9.4.5 Control Phase (p. 188)
• 9.5 Case Study Exercise (p. 189)
• 9.5.1 Project to Improve a Paper Air Wings System (p. 190)
• 9.6 Chapter Summary (p. 194)
• 9.7 References (p. 195)
• 9.8 Problems (p. 195)
• 10 SQC Theory (p. 199)
• 10.1 Introduction (p. 199)
• 10.2 Probability Theory (p. 200)
• 10.3 Continuous Random Variables (p. 203)
• 10.3.1 The Normal Probability Density Function (p. 207)
• 10.3.2 Defects Per Million Opportunities (p. 212)
• 10.3.3 Independent, Identically Distributed and Charting (p. 213)
• 10.3.4 The Central Limit Theorem (p. 216)
• 10.3.5 Advanced Topic: Deriving d[subscript 2] and c[subscript 4] (p. 219)
• 10.4 Discrete Random Variables (p. 220)
• 10.4.1 The Geometric and Hypergeometric Distributions (p. 222)
• 10.5 Xbar Charts and Average Run Length (p. 225)
• 10.5.1 The Chance of a Signal (p. 225)
• 10.5.2 Average Run Length (p. 227)
• 10.6 OC Curves and Average Sample Number (p. 229)
• 10.6.1 Single Sampling OC Curves (p. 230)
• 10.6.2 Double Sampling (p. 231)
• 10.6.3 Double Sampling Average Sample Number (p. 232)
• 10.7 Chapter Summary (p. 233)
• 10.8 References (p. 234)
• 10.9 Problems (p. 234)
• Part II Design of Experiments (DOE) and Regression
• 11 DOE: The Jewel of Quality Engineering (p. 241)
• 11.1 Introduction (p. 241)
• 11.2 Design of Experiments Methods Overview (p. 242)
• 11.2.1 Method Choices (p. 242)
• 11.3 The Two-sample T-test Methodology and the Word "Proven" (p. 243)
• 11.4 T-test Examples (p. 246)
• 11.4.1 Second T-test Application (p. 247)
• 11.5 Randomization and Evidence (p. 249)
• 11.5.1 Poor Randomization and Waste (p. 249)
• 11.6 Errors from DOE Procedures (p. 250)
• 11.6.1 Testing a New Drug (p. 252)
• 11.7 Chapter Summary (p. 252)
• 11.7.1 Student Retention Study (p. 253)
• 11.8 Problems (p. 254)
• 12 DOE: Screening Using Fractional Factorials (p. 259)
• 12.1 Introduction (p. 259)
• 12.2 Standard Screening Using Fractional Factorials (p. 260)
• 12.3 Screening Examples (p. 266)
• 12.3.1 More Detailed Application (p. 269)
• 12.4 Method Origins and Alternatives (p. 271)
• 12.4.1 Origins of the Arrays (p. 271)
• 12.4.2 Experimental Design Generation (p. 273)
• 12.4.3 Alternatives to the Methods in this Chapter (p. 273)
• 12.5 Standard vs One-factor-at-a-time Experimentation (p. 275)
• 12.5.1 Printed Circuit Board Related Method Choices (p. 277)
• 12.6 Chapter Summary (p. 277)
• 12.7 References (p. 277)
• 12.8 Problems (p. 278)
• 13 DOE: Response Surface Methods (p. 285)
• 13.1 Introduction (p. 285)
• 13.2 Design Matrices for Fitting RSM Models (p. 286)
• 13.2.1 Three Factor Full Quadratic (p. 286)
• 13.2.2 Multiple Functional Forms (p. 287)
• 13.3 One-shot Response Surface Methods (p. 288)
• 13.4 One-shot RSM Examples (p. 291)
• 13.4.1 Food Science Application (p. 298)
• 13.5 Creating 3D Surface Plots in Excel (p. 298)
• 13.6 Sequential Response Surface Methods (p. 299)
• 13.6.1 Lack of Fit (p. 303)
• 13.7 Origin of RSM Designs and Decision-making (p. 304)
• 13.7.1 Origins of the RSM Experimental Arrays (p. 304)
• 13.7.2 Decision Support Information (Optional) (p. 307)
• 13.8 Appendix: Additional Response Surface Designs (p. 310)
• 13.9 Chapter Summary (p. 315)
• 13.10 References (p. 315)
• 13.11 Problems (p. 316)
• 14 DOE: Robust Design (p. 321)
• 14.1 Introduction (p. 321)
• 14.2 Expected Profits and Control-by-noise Interactions (p. 323)
• 14.2.1 Polynomials in Standard Format (p. 324)
• 14.3 Robust Design Based on Profit Maximization (p. 325)
• 14.3.1 Example of RDPM and Central Composite Designs (p. 326)
• 14.3.2 RDPM and Six Sigma (p. 332)
• 14.4 Extended Taguchi Methods (p. 332)
• 14.4.1 Welding Process Design Example Revisited (p. 334)
• 14.5 Literature Review and Methods Comparison (p. 336)
• 14.6 Chapter Summary (p. 338)
• 14.7 References (p. 338)
• 14.8 Problems (p. 339)
• 15 Regression (p. 343)
• 15.1 Introduction (p. 343)
• 15.2 Single Variable Example (p. 344)
• 15.2.1 Demand Trend Analysis (p. 345)
• 15.2.2 The Least Squares Formula (p. 345)
• 15.3 Preparing "Flat Files" and Missing Data (p. 346)
• 15.3.1 Handling Missing Data (p. 347)
• 15.4 Evaluating Models and DOE Theory (p. 348)
• 15.4.1 Variance Inflation Factors and Correlation Matrices (p. 349)
• 15.4.2 Evaluating Data Quality (p. 350)
• 15.4.3 Normal Probability Plots and Other "Residual Plots" (p. 351)
• 15.4.4 Normal Probability Plotting Residuals (p. 353)
• 15.4.5 Summary Statistics (p. 356)
• 15.4.6 R[superscript 2] Adjusted Calculations (p. 356)
• 15.4.7 Calculating R[superscript 2] Prediction (p. 357)
• 15.4.8 Estimating Sigma Using Regression (p. 358)
• 15.5 Analysis of Variance Followed by Multiple T-tests (p. 359)
• 15.5.1 Single Factor ANOVA Application (p. 361)
• 15.6 Regression Modeling Flowchart (p. 362)
• 15.6.1 Method Choices (p. 363)
• 15.6.2 Body Fat Prediction (p. 364)
• 15.7 Categorical and Mixture Factors (Optional) (p. 367)
• 15.7.1 Regression with Categorical Factors (p. 368)
• 15.7.2 DOE with Categorical Inputs and Outputs (p. 369)
• 15.7.3 Recipe Factors or "Mixture Components" (p. 370)
• 15.7.4 Method Choices (p. 371)
• 15.8 Chapter Summary (p. 371)
• 15.9 References (p. 372)
• 15.10 Problems (p. 372)
• 16 Advanced Regression and Alternatives (p. 379)
• 16.1 Introduction (p. 379)
• 16.2 Generic Curve Fitting (p. 379)
• 16.2.1 Curve Fitting Example (p. 380)
• 16.3 Kriging Model and Computer Experiments (p. 381)
• 16.3.1 Design of Experiments for Kriging Models (p. 382)
• 16.3.2 Fitting Kriging Models (p. 382)
• 16.3.3 Kriging Single Variable Example (p. 385)
• 16.4 Neural Nets for Regression Type Problems (p. 385)
• 16.5 Logistics Regression and Discrete Choice Models (p. 391)
• 16.5.1 Design of Experiments for Logistic Regression (p. 393)
• 16.5.2 Fitting Logit Models (p. 394)
• 16.5.3 Paper Helicopter Logistic Regression Example (p. 395)
• 16.6 Chapter Summary (p. 397)
• 16.7 References (p. 397)
• 16.8 Problems (p. 398)
• 17 DOE and Regression Case Studies (p. 401)
• 17.1 Introduction (p. 401)
• 17.2 Case Study: the Rubber Machine (p. 401)
• 17.2.1 The Situation (p. 401)
• 17.2.2 Background Information (p. 402)
• 17.2.3 The Problem Statement (p. 402)
• 17.3 The Application of Formal Improvement Systems Technology (p. 403)
• 17.4 Case Study: Snap Tab Design Improvement (p. 407)
• 17.5 The Selection of the Factors (p. 410)
• 17.6 General Procedure for Low Cost Response Surface Methods (p. 411)
• 17.7 The Engineering Design of Snap Fits (p. 411)
• 17.8 Concept Review (p. 415)
• 17.9 Additional Discussion of Randomization (p. 416)
• 17.10 Chapter Summary (p. 418)
• 17.11 References (p. 419)
• 17.12 Problems (p. 419)
• 18 DOE and Regression Theory (p. 423)
• 18.1 Introduction (p. 423)
• 18.2 Design of Experiments Criteria (p. 424)
• 18.3 Generating "Pseudo-Random" Numbers (p. 425)
• 18.3.1 Other Distributions (p. 427)
• 18.3.2 Correlated Random Variables (p. 429)
• 18.3.3 Monte Carlo Simulation (Review) (p. 430)
• 18.3.4 The Law of the Unconscious Statistician (p. 431)
• 18.4 Simulating T-testing (p. 432)
• 18.4.1 Sample Size Determination for T-testing (p. 435)
• 18.5 Simulating Standard Screening Methods (p. 437)
• 18.6 Evaluating Response Surface Methods (p. 439)
• 18.6.1 Taylor Series and Reasonable Assumptions (p. 440)
• 18.6.2 Regression and Expected Prediction Errors (p. 441)
• 18.6.3 The EIMSE Formula (p. 444)
• 18.7 Chapter Summary (p. 450)
• 18.8 References (p. 451)
• 18.9 Problems (p. 451)
• Part III Optimization and Strategy
• 19 Optimization And Strategy (p. 457)
• 19.1 Introduction (p. 457)
• 19.2 Formal Optimization (p. 458)
• 19.2.1 Heuristics and Rigorous Methods (p. 461)
• 19.3 Stochastic Optimization (p. 463)
• 19.4 Genetic Algorithms (p. 466)
• 19.4.1 Genetic Algorithms for Stochastic Optimization (p. 465)
• 19.4.2 Populations, Cross-over, and Mutation (p. 466)
• 19.4.3 An Elitist Genetic Algorithm with Immigration (p. 467)
• 19.4.4 Test Stochastic Optimization Problems (p. 468)
• 19.5 Variants on the Proposed Methods (p. 469)
• 19.6 Appendix: C Code for "Toycoolga" (p. 470)
• 19.7 Chapter Summary (p. 474)
• 19.8 References (p. 474)
• 19.9 Problems (p. 475)
• 20 Tolerance Design (p. 479)
• 20.1 Introduction (p. 479)
• 20.2 Chapter Summary (p. 481)
• 20.3 References (p. 481)
• 20.4 Problems (p. 481)
• 21 Six Sigma Project Design (p. 483)
• 21.1 Introduction (p. 483)
• 21.2 Literature Review (p. 484)
• 21.3 Reverse Engineering Six Sigma (p. 485)
• 21.4 Uncovering and Solving Optimization Problems (p. 487)
• 21.5 Future Research Opportunities (p. 490)
• 21.5.1 New Methods from Stochastic Optimization (p. 491)
• 21.5.2 Meso-Analyses of Project Databases (p. 492)
• 21.5.3 Test Beds and Optimal Strategies (p. 494)
• 21.6 References (p. 495)
• 21.7 Problems (p. 496)
• Glossary (p. 499)
• Problem Solutions (p. 505)
• Index (p. 523)